Overview

Brought to you by YData

Dataset statistics

Number of variables18
Number of observations20758
Missing cells0
Missing cells (%)0.0%
Total size in memory2.8 MiB
Average record size in memory140.0 B

Variable types

Numeric10
Text8

Alerts

id is uniformly distributedUniform
id has unique valuesUnique
FAF has 5044 (24.3%) zerosZeros
TUE has 6566 (31.6%) zerosZeros
NObeyesdad has 2523 (12.2%) zerosZeros

Reproduction

Analysis started2024-11-03 16:46:07.742892
Analysis finished2024-11-03 16:46:49.154639
Duration41.41 seconds
Software versionydata-profiling vv4.10.0
Download configurationconfig.json

Variables

id
Real number (ℝ)

UNIFORM  UNIQUE 

Distinct20758
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10378.5
Minimum0
Maximum20757
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size162.3 KiB
2024-11-03T17:46:49.822247image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1037.85
Q15189.25
median10378.5
Q315567.75
95-th percentile19719.15
Maximum20757
Range20757
Interquartile range (IQR)10378.5

Descriptive statistics

Standard deviation5992.46278
Coefficient of variation (CV)0.5773919911
Kurtosis-1.2
Mean10378.5
Median Absolute Deviation (MAD)5189.5
Skewness0
Sum215436903
Variance35909610.17
MonotonicityStrictly increasing
2024-11-03T17:46:50.186318image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
13826 1
 
< 0.1%
13844 1
 
< 0.1%
13843 1
 
< 0.1%
13842 1
 
< 0.1%
13841 1
 
< 0.1%
13840 1
 
< 0.1%
13839 1
 
< 0.1%
13838 1
 
< 0.1%
13837 1
 
< 0.1%
Other values (20748) 20748
> 99.9%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
ValueCountFrequency (%)
20757 1
< 0.1%
20756 1
< 0.1%
20755 1
< 0.1%
20754 1
< 0.1%
20753 1
< 0.1%

Gender
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size162.3 KiB
2024-11-03T17:46:50.624094image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length6
Median length6
Mean length5.004142981
Min length4

Characters and Unicode

Total characters103876
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowMale
2nd rowFemale
3rd rowFemale
4th rowFemale
5th rowMale
ValueCountFrequency (%)
female 10422
50.2%
male 10336
49.8%
2024-11-03T17:46:51.467706image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 31180
30.0%
a 20758
20.0%
l 20758
20.0%
F 10422
 
10.0%
m 10422
 
10.0%
M 10336
 
10.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 83118
80.0%
Uppercase Letter 20758
 
20.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 31180
37.5%
a 20758
25.0%
l 20758
25.0%
m 10422
 
12.5%
Uppercase Letter
ValueCountFrequency (%)
F 10422
50.2%
M 10336
49.8%

Most occurring scripts

ValueCountFrequency (%)
Latin 103876
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 31180
30.0%
a 20758
20.0%
l 20758
20.0%
F 10422
 
10.0%
m 10422
 
10.0%
M 10336
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103876
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 31180
30.0%
a 20758
20.0%
l 20758
20.0%
F 10422
 
10.0%
m 10422
 
10.0%
M 10336
 
10.0%

Age
Real number (ℝ)

Distinct1703
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.84180442
Minimum14
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size162.3 KiB
2024-11-03T17:46:51.883101image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile17.98957735
Q120
median22.815416
Q326
95-th percentile35.46041725
Maximum61
Range47
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.688071959
Coefficient of variation (CV)0.238575565
Kurtosis3.700597749
Mean23.84180442
Median Absolute Deviation (MAD)3.184584
Skewness1.586251709
Sum494908.1761
Variance32.35416261
MonotonicityNot monotonic
2024-11-03T17:46:52.348547image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 1916
 
9.2%
26 1806
 
8.7%
21 1643
 
7.9%
23 1202
 
5.8%
19 886
 
4.3%
20 530
 
2.6%
17 516
 
2.5%
22 512
 
2.5%
33 209
 
1.0%
24 164
 
0.8%
Other values (1693) 11374
54.8%
ValueCountFrequency (%)
14 5
 
< 0.1%
15 3
 
< 0.1%
16 109
0.5%
16.093234 4
 
< 0.1%
16.120699 1
 
< 0.1%
ValueCountFrequency (%)
61 2
< 0.1%
56 1
< 0.1%
55.493687 1
< 0.1%
55.272573 1
< 0.1%
55.24625 2
< 0.1%

Height
Real number (ℝ)

Distinct1833
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.700244935
Minimum1.45
Maximum1.975663
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size162.3 KiB
2024-11-03T17:46:52.756315image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1.45
5-th percentile1.559005
Q11.631856
median1.7
Q31.762887
95-th percentile1.84629
Maximum1.975663
Range0.525663
Interquartile range (IQR)0.131031

Descriptive statistics

Standard deviation0.0873119057
Coefficient of variation (CV)0.05135254568
Kurtosis-0.5596340867
Mean1.700244935
Median Absolute Deviation (MAD)0.066055
Skewness0.01580267619
Sum35293.68436
Variance0.007623368876
MonotonicityNot monotonic
2024-11-03T17:46:53.165189image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.7 1334
 
6.4%
1.65 782
 
3.8%
1.6 672
 
3.2%
1.75 659
 
3.2%
1.8 517
 
2.5%
1.62 398
 
1.9%
1.72 317
 
1.5%
1.56 256
 
1.2%
1.63 239
 
1.2%
1.55 211
 
1.0%
Other values (1823) 15373
74.1%
ValueCountFrequency (%)
1.45 2
 
< 0.1%
1.456346 2
 
< 0.1%
1.463167 1
 
< 0.1%
1.48 9
< 0.1%
1.481682 1
 
< 0.1%
ValueCountFrequency (%)
1.975663 4
< 0.1%
1.947406 4
< 0.1%
1.942725 4
< 0.1%
1.931263 2
< 0.1%
1.931242 1
 
< 0.1%

Weight
Real number (ℝ)

Distinct1979
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.8877684
Minimum39
Maximum165.057269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size162.3 KiB
2024-11-03T17:46:53.527562image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile49
Q166
median84.064875
Q3111.600553
95-th percentile132.116491
Maximum165.057269
Range126.057269
Interquartile range (IQR)45.600553

Descriptive statistics

Standard deviation26.37944308
Coefficient of variation (CV)0.3001491966
Kurtosis-0.9970433336
Mean87.8877684
Median Absolute Deviation (MAD)22.9471315
Skewness0.09318727954
Sum1824374.297
Variance695.875017
MonotonicityNot monotonic
2024-11-03T17:46:54.021670image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 866
 
4.2%
75 630
 
3.0%
50 618
 
3.0%
60 506
 
2.4%
70 486
 
2.3%
45 323
 
1.6%
65 306
 
1.5%
85 297
 
1.4%
78 293
 
1.4%
42 275
 
1.3%
Other values (1969) 16158
77.8%
ValueCountFrequency (%)
39 1
 
< 0.1%
39.101805 5
< 0.1%
39.12631 1
 
< 0.1%
39.371523 2
 
< 0.1%
39.535047 1
 
< 0.1%
ValueCountFrequency (%)
165.057269 4
 
< 0.1%
160.935351 14
0.1%
160.639405 3
 
< 0.1%
155.872093 3
 
< 0.1%
155.242672 3
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size162.3 KiB
2024-11-03T17:46:54.413581image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.819635803
Min length2

Characters and Unicode

Total characters58530
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowyes
3rd rowyes
4th rowyes
5th rowyes
ValueCountFrequency (%)
yes 17014
82.0%
no 3744
 
18.0%
2024-11-03T17:46:55.136228image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
y 17014
29.1%
e 17014
29.1%
s 17014
29.1%
n 3744
 
6.4%
o 3744
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 58530
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
y 17014
29.1%
e 17014
29.1%
s 17014
29.1%
n 3744
 
6.4%
o 3744
 
6.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 58530
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
y 17014
29.1%
e 17014
29.1%
s 17014
29.1%
n 3744
 
6.4%
o 3744
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58530
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
y 17014
29.1%
e 17014
29.1%
s 17014
29.1%
n 3744
 
6.4%
o 3744
 
6.4%

FAVC
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size162.3 KiB
2024-11-03T17:46:55.485838image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.914442625
Min length2

Characters and Unicode

Total characters60498
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowyes
2nd rowyes
3rd rowyes
4th rowyes
5th rowyes
ValueCountFrequency (%)
yes 18982
91.4%
no 1776
 
8.6%
2024-11-03T17:46:56.137121image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
y 18982
31.4%
e 18982
31.4%
s 18982
31.4%
n 1776
 
2.9%
o 1776
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 60498
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
y 18982
31.4%
e 18982
31.4%
s 18982
31.4%
n 1776
 
2.9%
o 1776
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
Latin 60498
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
y 18982
31.4%
e 18982
31.4%
s 18982
31.4%
n 1776
 
2.9%
o 1776
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60498
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
y 18982
31.4%
e 18982
31.4%
s 18982
31.4%
n 1776
 
2.9%
o 1776
 
2.9%

FCVC
Real number (ℝ)

Distinct934
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.445908393
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size162.3 KiB
2024-11-03T17:46:56.555726image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.826885
Q12
median2.393837
Q33
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5332181545
Coefficient of variation (CV)0.2180041395
Kurtosis-0.8929961324
Mean2.445908393
Median Absolute Deviation (MAD)0.408223
Skewness-0.356611247
Sum50772.16642
Variance0.2843216002
MonotonicityNot monotonic
2024-11-03T17:46:56.983644image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 7658
36.9%
2 7653
36.9%
1 275
 
1.3%
2.9673 121
 
0.6%
2.766612 54
 
0.3%
2.938616 46
 
0.2%
2.9553 39
 
0.2%
2.57649 30
 
0.1%
2.819934 30
 
0.1%
2.225149 29
 
0.1%
Other values (924) 4823
23.2%
ValueCountFrequency (%)
1 275
1.3%
1.002564 1
 
< 0.1%
1.003566 6
 
< 0.1%
1.005578 13
 
0.1%
1.006436 1
 
< 0.1%
ValueCountFrequency (%)
3 7658
36.9%
2.998441 2
 
< 0.1%
2.997951 11
 
0.1%
2.997524 5
 
< 0.1%
2.997062 1
 
< 0.1%

NCP
Real number (ℝ)

Distinct689
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.761332307
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size162.3 KiB
2024-11-03T17:46:57.493092image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median3
Q33
95-th percentile3.520555
Maximum4
Range3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.7053745959
Coefficient of variation (CV)0.2554471963
Kurtosis1.837269616
Mean2.761332307
Median Absolute Deviation (MAD)0
Skewness-1.562253291
Sum57319.73602
Variance0.4975533205
MonotonicityNot monotonic
2024-11-03T17:46:57.903071image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 14706
70.8%
1 1976
 
9.5%
4 715
 
3.4%
2.993623 39
 
0.2%
2.695396 25
 
0.1%
2.977909 22
 
0.1%
2.992083 21
 
0.1%
1.894384 21
 
0.1%
2.658837 20
 
0.1%
2.938902 20
 
0.1%
Other values (679) 3193
 
15.4%
ValueCountFrequency (%)
1 1976
9.5%
1.000283 5
 
< 0.1%
1.000414 2
 
< 0.1%
1.00061 7
 
< 0.1%
1.001383 6
 
< 0.1%
ValueCountFrequency (%)
4 715
3.4%
3.998766 3
 
< 0.1%
3.998618 6
 
< 0.1%
3.995957 5
 
< 0.1%
3.995147 5
 
< 0.1%

CAEC
Text

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size162.3 KiB
2024-11-03T17:46:58.423323image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length10
Median length9
Mean length8.955920609
Min length2

Characters and Unicode

Total characters185907
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSometimes
2nd rowFrequently
3rd rowSometimes
4th rowSometimes
5th rowSometimes
ValueCountFrequency (%)
sometimes 17529
84.4%
frequently 2472
 
11.9%
always 478
 
2.3%
no 279
 
1.3%
2024-11-03T17:46:59.330150image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 40002
21.5%
m 35058
18.9%
t 20001
10.8%
s 18007
9.7%
o 17808
9.6%
S 17529
9.4%
i 17529
9.4%
y 2950
 
1.6%
l 2950
 
1.6%
n 2751
 
1.5%
Other values (7) 11322
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 165428
89.0%
Uppercase Letter 20479
 
11.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 40002
24.2%
m 35058
21.2%
t 20001
12.1%
s 18007
10.9%
o 17808
10.8%
i 17529
10.6%
y 2950
 
1.8%
l 2950
 
1.8%
n 2751
 
1.7%
r 2472
 
1.5%
Other values (4) 5900
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
S 17529
85.6%
F 2472
 
12.1%
A 478
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 185907
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 40002
21.5%
m 35058
18.9%
t 20001
10.8%
s 18007
9.7%
o 17808
9.6%
S 17529
9.4%
i 17529
9.4%
y 2950
 
1.6%
l 2950
 
1.6%
n 2751
 
1.5%
Other values (7) 11322
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 185907
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 40002
21.5%
m 35058
18.9%
t 20001
10.8%
s 18007
9.7%
o 17808
9.6%
S 17529
9.4%
i 17529
9.4%
y 2950
 
1.6%
l 2950
 
1.6%
n 2751
 
1.5%
Other values (7) 11322
 
6.1%

SMOKE
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size162.3 KiB
2024-11-03T17:46:59.647798image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.011802678
Min length2

Characters and Unicode

Total characters41761
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno
ValueCountFrequency (%)
no 20513
98.8%
yes 245
 
1.2%
2024-11-03T17:47:00.325739image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 20513
49.1%
o 20513
49.1%
y 245
 
0.6%
e 245
 
0.6%
s 245
 
0.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 41761
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 20513
49.1%
o 20513
49.1%
y 245
 
0.6%
e 245
 
0.6%
s 245
 
0.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 41761
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 20513
49.1%
o 20513
49.1%
y 245
 
0.6%
e 245
 
0.6%
s 245
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41761
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 20513
49.1%
o 20513
49.1%
y 245
 
0.6%
e 245
 
0.6%
s 245
 
0.6%

CH2O
Real number (ℝ)

Distinct1506
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.029418244
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size162.3 KiB
2024-11-03T17:47:00.743937image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.792022
median2
Q32.549617
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)0.757595

Descriptive statistics

Standard deviation0.6084670185
Coefficient of variation (CV)0.2998233707
Kurtosis-0.7441799892
Mean2.029418244
Median Absolute Deviation (MAD)0.409582
Skewness-0.2125058334
Sum42126.6639
Variance0.3702321125
MonotonicityNot monotonic
2024-11-03T17:47:01.203394image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 6616
31.9%
1 2799
 
13.5%
3 1571
 
7.6%
2.825629 77
 
0.4%
2.868167 60
 
0.3%
2.619517 57
 
0.3%
2.625537 56
 
0.3%
2.72005 52
 
0.3%
2.770732 51
 
0.2%
2.613928 47
 
0.2%
Other values (1496) 9372
45.1%
ValueCountFrequency (%)
1 2799
13.5%
1.000463 5
 
< 0.1%
1.000536 4
 
< 0.1%
1.000544 7
 
< 0.1%
1.000695 3
 
< 0.1%
ValueCountFrequency (%)
3 1571
7.6%
2.999495 3
 
< 0.1%
2.99675 1
 
< 0.1%
2.994515 2
 
< 0.1%
2.993448 1
 
< 0.1%

SCC
Text

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size162.3 KiB
2024-11-03T17:47:01.513737image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.033095674
Min length2

Characters and Unicode

Total characters42203
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowno
2nd rowno
3rd rowno
4th rowno
5th rowno
ValueCountFrequency (%)
no 20071
96.7%
yes 687
 
3.3%
2024-11-03T17:47:02.189500image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 20071
47.6%
o 20071
47.6%
y 687
 
1.6%
e 687
 
1.6%
s 687
 
1.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 42203
100.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 20071
47.6%
o 20071
47.6%
y 687
 
1.6%
e 687
 
1.6%
s 687
 
1.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 42203
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 20071
47.6%
o 20071
47.6%
y 687
 
1.6%
e 687
 
1.6%
s 687
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42203
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 20071
47.6%
o 20071
47.6%
y 687
 
1.6%
e 687
 
1.6%
s 687
 
1.6%

FAF
Real number (ℝ)

ZEROS 

Distinct1360
Distinct (%)6.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9817465551
Minimum0
Maximum3
Zeros5044
Zeros (%)24.3%
Negative0
Negative (%)0.0%
Memory size162.3 KiB
2024-11-03T17:47:02.569049image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.008013
median1
Q31.587406
95-th percentile2.545707
Maximum3
Range3
Interquartile range (IQR)1.579393

Descriptive statistics

Standard deviation0.838301976
Coefficient of variation (CV)0.8538883805
Kurtosis-0.4948424237
Mean0.9817465551
Median Absolute Deviation (MAD)0.870098
Skewness0.5057262218
Sum20379.09499
Variance0.7027502029
MonotonicityNot monotonic
2024-11-03T17:47:02.986433image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5044
24.3%
1 4097
19.7%
2 2391
 
11.5%
3 800
 
3.9%
1.097905 56
 
0.3%
1.427037 47
 
0.2%
0.01586 47
 
0.2%
1.68249 39
 
0.2%
1.999836 36
 
0.2%
1.465931 32
 
0.2%
Other values (1350) 8169
39.4%
ValueCountFrequency (%)
0 5044
24.3%
9.6 × 10-510
 
< 0.1%
0.000272 9
 
< 0.1%
0.000454 11
 
0.1%
0.001015 12
 
0.1%
ValueCountFrequency (%)
3 800
3.9%
2.999918 3
 
< 0.1%
2.993666 1
 
< 0.1%
2.977543 1
 
< 0.1%
2.971832 3
 
< 0.1%

TUE
Real number (ℝ)

ZEROS 

Distinct1297
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6167562237
Minimum0
Maximum2
Zeros6566
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size162.3 KiB
2024-11-03T17:47:03.383625image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.573887
Q31
95-th percentile2
Maximum2
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.602113477
Coefficient of variation (CV)0.9762584533
Kurtosis-0.4177297989
Mean0.6167562237
Median Absolute Deviation (MAD)0.450026
Skewness0.6704113441
Sum12802.62569
Variance0.3625406392
MonotonicityNot monotonic
2024-11-03T17:47:03.820871image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6566
31.6%
1 4326
20.8%
2 1133
 
5.5%
0.0026 78
 
0.4%
0.723154 65
 
0.3%
0.088236 53
 
0.3%
0.15171 52
 
0.3%
0.630866 45
 
0.2%
0.62535 41
 
0.2%
0.200379 38
 
0.2%
Other values (1287) 8361
40.3%
ValueCountFrequency (%)
0 6566
31.6%
7.3 × 10-52
 
< 0.1%
0.000355 2
 
< 0.1%
0.000436 3
 
< 0.1%
0.001096 5
 
< 0.1%
ValueCountFrequency (%)
2 1133
5.5%
1.99219 2
 
< 0.1%
1.990925 1
 
< 0.1%
1.990617 4
 
< 0.1%
1.983678 1
 
< 0.1%

CALC
Text

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size162.3 KiB
2024-11-03T17:47:04.308475image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length10
Median length9
Mean length7.284420464
Min length2

Characters and Unicode

Total characters151210
Distinct characters14
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSometimes
2nd rowno
3rd rowno
4th rowSometimes
5th rowSometimes
ValueCountFrequency (%)
sometimes 15066
72.6%
no 5163
 
24.9%
frequently 529
 
2.5%
2024-11-03T17:47:05.110634image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 31190
20.6%
m 30132
19.9%
o 20229
13.4%
t 15595
10.3%
S 15066
10.0%
i 15066
10.0%
s 15066
10.0%
n 5692
 
3.8%
F 529
 
0.3%
r 529
 
0.3%
Other values (4) 2116
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 135615
89.7%
Uppercase Letter 15595
 
10.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 31190
23.0%
m 30132
22.2%
o 20229
14.9%
t 15595
11.5%
i 15066
11.1%
s 15066
11.1%
n 5692
 
4.2%
r 529
 
0.4%
q 529
 
0.4%
u 529
 
0.4%
Other values (2) 1058
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
S 15066
96.6%
F 529
 
3.4%

Most occurring scripts

ValueCountFrequency (%)
Latin 151210
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 31190
20.6%
m 30132
19.9%
o 20229
13.4%
t 15595
10.3%
S 15066
10.0%
i 15066
10.0%
s 15066
10.0%
n 5692
 
3.8%
F 529
 
0.3%
r 529
 
0.3%
Other values (4) 2116
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 151210
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 31190
20.6%
m 30132
19.9%
o 20229
13.4%
t 15595
10.3%
S 15066
10.0%
i 15066
10.0%
s 15066
10.0%
n 5692
 
3.8%
F 529
 
0.3%
r 529
 
0.3%
Other values (4) 2116
 
1.4%

MTRANS
Text

Distinct5
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size162.3 KiB
2024-11-03T17:47:05.667808image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Length

Max length21
Median length21
Mean length18.76413913
Min length4

Characters and Unicode

Total characters389506
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPublic_Transportation
2nd rowAutomobile
3rd rowPublic_Transportation
4th rowPublic_Transportation
5th rowPublic_Transportation
ValueCountFrequency (%)
public_transportation 16687
80.4%
automobile 3534
 
17.0%
walking 467
 
2.2%
motorbike 38
 
0.2%
bike 32
 
0.2%
2024-11-03T17:47:06.521761image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 40518
10.4%
i 37445
 
9.6%
t 36946
 
9.5%
a 33841
 
8.7%
n 33841
 
8.7%
r 33412
 
8.6%
l 20688
 
5.3%
b 20259
 
5.2%
u 20221
 
5.2%
P 16687
 
4.3%
Other values (13) 95648
24.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 335374
86.1%
Uppercase Letter 37445
 
9.6%
Connector Punctuation 16687
 
4.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 40518
12.1%
i 37445
11.2%
t 36946
11.0%
a 33841
10.1%
n 33841
10.1%
r 33412
10.0%
l 20688
6.2%
b 20259
6.0%
u 20221
6.0%
p 16687
5.0%
Other values (6) 41516
12.4%
Uppercase Letter
ValueCountFrequency (%)
P 16687
44.6%
T 16687
44.6%
A 3534
 
9.4%
W 467
 
1.2%
M 38
 
0.1%
B 32
 
0.1%
Connector Punctuation
ValueCountFrequency (%)
_ 16687
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 372819
95.7%
Common 16687
 
4.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 40518
10.9%
i 37445
10.0%
t 36946
9.9%
a 33841
9.1%
n 33841
9.1%
r 33412
9.0%
l 20688
 
5.5%
b 20259
 
5.4%
u 20221
 
5.4%
P 16687
 
4.5%
Other values (12) 78961
21.2%
Common
ValueCountFrequency (%)
_ 16687
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 389506
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 40518
10.4%
i 37445
 
9.6%
t 36946
 
9.5%
a 33841
 
8.7%
n 33841
 
8.7%
r 33412
 
8.6%
l 20688
 
5.3%
b 20259
 
5.2%
u 20221
 
5.2%
P 16687
 
4.3%
Other values (13) 95648
24.6%

NObeyesdad
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.991473167
Minimum0
Maximum6
Zeros2523
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size81.2 KiB
2024-11-03T17:47:06.822367image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q34
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.893176103
Coefficient of variation (CV)0.6328574576
Kurtosis-1.111076134
Mean2.991473167
Median Absolute Deviation (MAD)2
Skewness-0.01581985557
Sum62097
Variance3.584115756
MonotonicityNot monotonic
2024-11-03T17:47:07.086223image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 4046
19.5%
3 3248
15.6%
1 3082
14.8%
2 2910
14.0%
0 2523
12.2%
6 2522
12.1%
5 2427
11.7%
ValueCountFrequency (%)
0 2523
12.2%
1 3082
14.8%
2 2910
14.0%
3 3248
15.6%
4 4046
19.5%
ValueCountFrequency (%)
6 2522
12.1%
5 2427
11.7%
4 4046
19.5%
3 3248
15.6%
2 2910
14.0%

Interactions

2024-11-03T17:46:44.418370image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:11.222994image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:14.392461image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:17.867296image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:21.982302image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:25.953654image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:29.830316image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:33.549702image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:37.487389image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:40.773408image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:44.741641image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:11.467945image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:14.713839image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:18.193242image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:22.357324image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:26.287345image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:30.135130image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:33.884344image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:37.805730image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:41.102707image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:45.064270image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:11.738082image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:15.022250image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:18.506270image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:22.853110image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:26.609215image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:30.470630image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:34.217206image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:38.116991image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:41.436914image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:45.373149image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:12.074982image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:15.324058image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:19.243856image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:23.208423image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:26.968037image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:30.803225image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:34.580283image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:38.439124image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:41.796631image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:45.682847image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:12.435719image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:15.695525image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:19.564221image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:23.541556image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:27.372044image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:31.188656image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:34.944521image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:38.766972image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:42.129103image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:46.041159image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:12.771842image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:16.095196image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:19.988193image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:23.921585image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:27.753660image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:31.587780image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:35.287637image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:39.120002image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:42.554177image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:46.391299image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:13.080808image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:16.394681image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:20.331553image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:24.464009image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:28.117687image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:31.903056image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:35.657989image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:39.429351image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:42.912385image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:46.724437image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:13.428473image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:16.738416image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:20.720019image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:24.871177image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:28.484090image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:32.542808image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:36.311985image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:39.739590image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:43.280157image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:47.049684image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:13.739945image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:17.109945image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:21.124743image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:25.211255image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:28.964867image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:32.854150image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:36.695533image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:40.085359image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:43.665843image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:47.412271image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:14.072889image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:17.499555image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:21.635220image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:25.596376image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:29.378027image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:33.242076image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:37.076222image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:40.450922image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
2024-11-03T17:46:44.057511image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/

Correlations

2024-11-03T17:47:07.362538image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
AgeCH2OFAFFCVCHeightNCPNObeyesdadTUEWeightid
Age1.0000.093-0.2780.0970.016-0.1200.323-0.3030.4410.010
CH2O0.0931.0000.0580.1070.1880.0830.182-0.0000.3490.012
FAF-0.2780.0581.000-0.0950.3180.111-0.103-0.011-0.0710.017
FCVC0.0970.107-0.0951.000-0.1150.1340.056-0.1300.2250.005
Height0.0160.1880.318-0.1151.0000.1090.0430.0840.4200.011
NCP-0.1200.0830.1110.1340.1091.000-0.1460.128-0.0220.001
NObeyesdad0.3230.182-0.1030.0560.043-0.1461.000-0.0680.4290.012
TUE-0.303-0.000-0.011-0.1300.0840.128-0.0681.000-0.0660.008
Weight0.4410.349-0.0710.2250.420-0.0220.429-0.0661.0000.014
id0.0100.0120.0170.0050.0110.0010.0120.0080.0141.000
2024-11-03T17:47:07.791181image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
idAgeHeightWeightFCVCNCPCH2OFAFTUENObeyesdad
id1.0000.0080.0120.0140.002-0.0000.0080.0170.0080.012
Age0.0081.000-0.0120.2830.034-0.048-0.016-0.192-0.2960.283
Height0.012-0.0121.0000.417-0.0720.1910.1840.2950.0760.061
Weight0.0140.2830.4171.0000.2460.0960.318-0.085-0.0860.432
FCVC0.0020.034-0.0720.2461.0000.1130.101-0.090-0.1480.041
NCP-0.000-0.0480.1910.0960.1131.0000.0810.1010.067-0.091
CH2O0.008-0.0160.1840.3180.1010.0811.0000.083-0.0110.187
FAF0.017-0.1920.295-0.085-0.0900.1010.0831.0000.021-0.097
TUE0.008-0.2960.076-0.086-0.1480.067-0.0110.0211.000-0.076
NObeyesdad0.0120.2830.0610.4320.041-0.0910.187-0.097-0.0761.000
2024-11-03T17:47:08.226649image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
idAgeHeightWeightFCVCNCPCH2OFAFTUENObeyesdad
id1.0000.0100.0110.0140.0050.0010.0120.0170.0080.012
Age0.0101.0000.0160.4410.097-0.1200.093-0.278-0.3030.323
Height0.0110.0161.0000.420-0.1150.1090.1880.3180.0840.043
Weight0.0140.4410.4201.0000.225-0.0220.349-0.071-0.0660.429
FCVC0.0050.097-0.1150.2251.0000.1340.107-0.095-0.1300.056
NCP0.001-0.1200.109-0.0220.1341.0000.0830.1110.128-0.146
CH2O0.0120.0930.1880.3490.1070.0831.0000.058-0.0000.182
FAF0.017-0.2780.318-0.071-0.0950.1110.0581.000-0.011-0.103
TUE0.008-0.3030.084-0.066-0.1300.128-0.000-0.0111.000-0.068
NObeyesdad0.0120.3230.0430.4290.056-0.1460.182-0.103-0.0681.000
2024-11-03T17:47:08.703528image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
idAgeHeightWeightFCVCNCPCH2OFAFTUENObeyesdad
id1.0000.0070.0070.0100.0030.0000.0090.0120.0060.009
Age0.0071.0000.0120.2960.067-0.0920.065-0.199-0.2290.234
Height0.0070.0121.0000.287-0.0840.0830.1350.2230.0590.026
Weight0.0100.2960.2871.0000.174-0.0080.244-0.043-0.0420.317
FCVC0.0030.067-0.0840.1741.0000.1170.085-0.076-0.0960.063
NCP0.000-0.0920.083-0.0080.1171.0000.0660.0890.106-0.116
CH2O0.0090.0650.1350.2440.0850.0661.0000.0420.0020.145
FAF0.012-0.1990.223-0.043-0.0760.0890.0421.000-0.004-0.076
TUE0.006-0.2290.059-0.042-0.0960.1060.002-0.0041.000-0.040
NObeyesdad0.0090.2340.0260.3170.063-0.1160.145-0.076-0.0401.000
2024-11-03T17:47:09.205200image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
idGenderAgeHeightWeightfamily_history_with_overweightFAVCFCVCNCPCAECSMOKECH2OSCCFAFTUECALCMTRANSNObeyesdad
id1.0000.0000.0000.0150.0170.0000.0060.0060.0170.0000.0090.0160.0000.0250.0000.0000.0120.029
Gender0.0001.0000.3400.8060.6700.1490.0320.5220.2090.1070.0980.4120.0950.4520.2770.0520.1380.575
Age0.0000.3401.0000.4500.6830.3920.1660.4140.3310.2580.1850.4620.1520.4340.5190.3100.7000.591
Height0.0150.8060.4501.0000.6770.3910.2000.3430.3090.2370.1440.4460.1910.4640.3510.2060.2010.481
Weight0.0170.6700.6830.6771.0000.7460.3110.5600.4760.4890.1000.6190.2670.6750.5580.4370.3560.847
family_history_with_overweight0.0000.1490.3920.3910.7461.0000.2360.1560.2910.4960.0260.3230.2580.2450.2690.0090.1080.518
FAVC0.0060.0320.1660.2000.3110.2361.0000.1080.0630.2050.0210.1790.1740.1800.1800.0710.1010.255
FCVC0.0060.5220.4140.3430.5600.1560.1081.0000.2790.1520.0360.3930.0480.3790.3730.2670.2140.544
NCP0.0170.2090.3310.3090.4760.2910.0630.2791.0000.2620.0130.2690.0860.3410.2660.2160.1330.407
CAEC0.0000.1070.2580.2370.4890.4960.2050.1520.2621.0000.0310.2320.1930.1990.2070.1040.0860.459
SMOKE0.0090.0980.1850.1440.1000.0260.0210.0360.0130.0311.0000.0640.0230.0500.0350.0130.0350.094
CH2O0.0160.4120.4620.4460.6190.3230.1790.3930.2690.2320.0641.0000.0900.4520.4270.2600.2070.488
SCC0.0000.0950.1520.1910.2670.2580.1740.0480.0860.1930.0230.0901.0000.1010.0910.0000.0400.207
FAF0.0250.4520.4340.4640.6750.2450.1800.3790.3410.1990.0500.4520.1011.0000.4270.2500.2510.471
TUE0.0000.2770.5190.3510.5580.2690.1800.3730.2660.2070.0350.4270.0910.4271.0000.2700.3020.454
CALC0.0000.0520.3100.2060.4370.0090.0710.2670.2160.1040.0130.2600.0000.2500.2701.0000.0990.419
MTRANS0.0120.1380.7000.2010.3560.1080.1010.2140.1330.0860.0350.2070.0400.2510.3020.0991.0000.257
NObeyesdad0.0290.5750.5910.4810.8470.5180.2550.5440.4070.4590.0940.4880.2070.4710.4540.4190.2571.000

Missing values

2024-11-03T17:46:48.000038image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-03T17:46:48.789975image/svg+xmlMatplotlib v3.7.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.